mlsanityData to 7 Oct 2026

FCAF3D: Fully Convolutional Anchor-Free 3D Object Detection

arXiv 2112.00322 ↗First posted 1 Dec 2021cs.CVcited by 97

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Abstract (arXiv)

Recently, promising applications in robotics and augmented reality have attracted considerable attention to 3D object detection from point clouds. In this paper, we present FCAF3D - a first-in-class fully convolutional anchor-free indoor 3D object detection method. It is a simple yet effective method that uses a voxel representation of a point cloud and processes voxels with sparse convolutions. FCAF3D can handle large-scale scenes with minimal runtime through a single fully convolutional feed-forward pass. Existing 3D object detection methods make prior assumptions on the geometry of objects, and we argue that it limits their generalization ability. To get rid of any prior assumptions, we propose a novel parametrization of oriented bounding boxes that allows obtaining better results in a purely data-driven way. The proposed method achieves state-of-the-art 3D object detection results in terms of [email protected] on ScanNet V2 (+4.5), SUN RGB-D (+3.5), and S3DIS (+20.5) datasets. The code and models are available at https://github.com/samsunglabs/fcaf3d.

Key facts. Cited by 97 papers in the map (data through 7 Oct 2026). Builds on 27 of its 38 references that are in the map. First posted on arXiv 1 Dec 2021.

Builds on

27 of 38 references.

References · 27

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  3. 2017ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenescited by 3,088
  4. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,707
  5. 2019FCOS: Fully Convolutional One-Stage Object Detectioncited by 1,573
  6. 2019MMDetection: Open MMLab Detection Toolbox and Benchmarkcited by 1,379
  7. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  8. 20194D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networkscited by 1,201
  9. 2020Center-based 3D Object Detection and Trackingcited by 1,143
  10. 2019Deep Hough Voting for 3D Object Detection in Point Cloudscited by 648
  11. 2019Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selectioncited by 539
  12. 2021FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detectioncited by 444
15 more
  1. 2021An End-to-End Transformer Model for 3D Object Detectioncited by 329
  2. 20183D-SIS: 3D Semantic Instance Segmentation of RGB-D Scanscited by 216
  3. 2021Group-Free 3D Object Detection via Transformerscited by 215
  4. 2020SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimationcited by 202
  5. 2021OTA: Optimal Transport Assignment for Object Detectioncited by 177
  6. 2021ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detectioncited by 157
  7. 20203D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentationcited by 140
  8. 2019IoU Loss for 2D/3D Object Detectioncited by 128
  9. 2020H3DNet: 3D Object Detection Using Hybrid Geometric Primitivescited by 123
  10. 2020ImVoteNet: Boosting 3D Object Detection in Point Clouds with Image Votescited by 119
  11. 2020MLCVNet: Multi-Level Context VoteNet for 3D Object Detectioncited by 80
  12. 2020Generative Sparse Detection Networks for 3D Single-shot Object Detectioncited by 69
  13. 2021Back-tracing Representative Points for Voting-based 3D Object Detection in Point Cloudscited by 67
  14. 20163D Fully Convolutional Network for Vehicle Detection in Point Cloudcited by 57
  15. 2019Frustum VoxNet for 3D object detection from RGB-D or Depth imagescited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 11; 2023: 28; 2024: 25; 2025: 18; 2026: 15.

97 papers.

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  1. 2023Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Drivingcited by 325
  2. 2022Mask3D: Mask Transformer for 3D Semantic Instance Segmentationcited by 299
  3. 2022Masked Discrimination for Self-Supervised Learning on Point Cloudscited by 155
  4. 2023EmbodiedScan: A Holistic Multi-Modal 3D Perception Suite Towards Embodied AIcited by 146
  5. 2023Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understandingcited by 111
  6. 2023OctFormer: Octree-based Transformers for 3D Point Cloudscited by 105
  7. 2023OneFormer3D: One Transformer for Unified Point Cloud Segmentationcited by 95
  8. 2024Voxel Mamba: Group-Free State Space Models for Point Cloud based 3D Object Detectioncited by 66
  9. 2025SpatialLM: Training Large Language Models for Structured Indoor Modelingcited by 63
  10. 2022CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Cloudscited by 58
  11. 2023PiMAE: Point Cloud and Image Interactive Masked Autoencoders for 3D Object Detectioncited by 53
  12. 2023NeRF-Det: Learning Geometry-Aware Volumetric Representation for Multi-View 3D Object Detectioncited by 51
38 more
  1. 2022Implicit Autoencoder for Point-Cloud Self-Supervised Representation Learningcited by 46
  2. 2022NeRF-RPN: A general framework for object detection in NeRFscited by 46
  3. 2023Mask-Attention-Free Transformer for 3D Instance Segmentationcited by 45
  4. 2023HEDNet: A Hierarchical Encoder-Decoder Network for 3D Object Detection in Point Cloudscited by 45
  5. 2024EmbodiedOcc: Embodied 3D Occupancy Prediction for Vision-based Online Scene Understandingcited by 43
  6. 2024Cubify Anything: Scaling Indoor 3D Object Detectioncited by 39
  7. 2023V-DETR: DETR with Vertex Relative Position Encoding for 3D Object Detectioncited by 34
  8. 2023TR3D: Towards Real-Time Indoor 3D Object Detectioncited by 32
  9. 2023Top-Down Beats Bottom-Up in 3D Instance Segmentationcited by 31
  10. 2023Uni3DETR: Unified 3D Detection Transformercited by 26
  11. 2023Octavius: Mitigating Task Interference in MLLMs via LoRA-MoEcited by 25
  12. 2024Uni3D-LLM: Unifying Point Cloud Perception, Generation and Editing with Large Language Modelscited by 24
  13. 2024NeRF-MAE: Masked AutoEncoders for Self-Supervised 3D Representation Learning for Neural Radiance Fieldscited by 20
  14. 2024OV-Uni3DETR: Towards Unified Open-Vocabulary 3D Object Detection via Cycle-Modality Propagationcited by 19
  15. 2025TSP3D: Text-guided Sparse Voxel Pruning for Efficient 3D Visual Groundingcited by 17
  16. 2023DQS3D: Densely-matched Quantization-aware Semi-supervised 3D Detectioncited by 16
  17. 2023ConDaFormer: Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understandingcited by 16
  18. 2023Point-GCC: Universal Self-supervised 3D Scene Pre-training via Geometry-Color Contrastcited by 14
  19. 2023ImGeoNet: Image-induced Geometry-aware Voxel Representation for Multi-view 3D Object Detectioncited by 14
  20. 2023Diffusion-SS3D: Diffusion Model for Semi-supervised 3D Object Detectioncited by 14
  21. 20233D Feature Prediction for Masked-AutoEncoder-Based Point Cloud Pretrainingcited by 13
  22. 2024Memory-based Adapters for Online 3D Scene Perceptioncited by 13
  23. 20243DGS-DET: Empower 3D Gaussian Splatting with Boundary Guidance and Box-Focused Sampling for Indoor 3D Object Detectioncited by 13
  24. 2022Boosting 3D Object Detection via Object-Focused Image Fusioncited by 11
  25. 2022TO-Scene: A Large-scale Dataset for Understanding 3D Tabletop Scenescited by 10
  26. 2024BIP3D: Bridging 2D Images and 3D Perception for Embodied Intelligencecited by 10
  27. 2024BSNet: Box-Supervised Simulation-assisted Mean Teacher for 3D Instance Segmentationcited by 9
  28. 2024One for All: Multi-Domain Joint Training for Point Cloud Based 3D Object Detectioncited by 8
  29. 2024CN-RMA: Combined Network with Ray Marching Aggregation for 3D Indoors Object Detection from Multi-view Imagescited by 7
  30. 2024FastCAD: Real-Time CAD Retrieval and Alignment from Scans and Videoscited by 7
  31. 2024General Geometry-aware Weakly Supervised 3D Object Detectioncited by 7
  32. 2024MVSDet: Multi-View Indoor 3D Object Detection via Efficient Plane Sweepscited by 7
  33. 2025OpenM3D: Open Vocabulary Multi-view Indoor 3D Object Detection without Human Annotationscited by 7
  34. 2022Autoregressive Uncertainty Modeling for 3D Bounding Box Predictioncited by 6
  35. 2022Learning Point-Language Hierarchical Alignment for 3D Visual Groundingcited by 6
  36. 2023GaPro: Box-Supervised 3D Point Cloud Instance Segmentation Using Gaussian Processes as Pseudo Labelerscited by 6
  37. 2023Not Every Side Is Equal: Localization Uncertainty Estimation for Semi-Supervised 3D Object Detectioncited by 6
  38. 2024Swin3D++: Effective Multi-Source Pretraining for 3D Indoor Scene Understandingcited by 6

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